I’m trying to understand how the particle alignment process works during ab-initio reconstruction. Specifically, I’m looking to understand how “subunit 1” is assigned for the alignment and reconstruction and whether this could be different between each particle.
For my example, I am working on a pentameric protein that is asymmetric, with 2 subunits moving outward and 3 mostly stationary compared to the symmetric state (Think pentagon shape, with each subunit in the corner vs. trapezoidal shape in the asymmetric state or look at the image below). If subunit 1 is assigned differently between particles, the alignment would likely be off and lead to poor averaging and should lead to a loss of “features” in the “outward” subunits.
Is there a way to verify that the alignment is done correctly or a way to alter the alignment “settings” during the ab-initio reconstruction to see if the reconstruction improves?
Hi clarknd,
I have a recent example that I hope will help. I was processing a dataset with two types of filaments, one with three strands and one with four. In both filaments there was a common pair of strands. When the filaments were processed together in a single reconstruction, those two strands refined well, while the other one or two strands were significantly averaged out. I could then separate the strands using one of a few different strategies to process each state separately. I would think a similar thing would happen with your data set assuming the quality of the data is good.
It’s worth pointing out that those common two filaments are not assigned in anyway, the four strand filament is symmetrical so any of the four strands could be considered subunit 1 in your example. I imagine if there are two different subsets of particles, one with subunits 1 and 2 aligned with subunits 2 and 3, for example, in your reconstruction you should be able to sort that out with 3D classification. But assuming you have two different complex states, you should be able to do an ab initio with at least two classes and separate them for independent processing. You could then use volume alignment tools or something like chimera (vop resample… after fit in map) to align the volumes to each other however you prefer.
